Python development

Python: the strongest choice for data-heavy projects and AI backends

Enterprise data management with Django, sub-millisecond API response with FastAPI — two frameworks, one expert team.

Quick answer

Python development service: Django for enterprise backend, FastAPI for high-performance APIs, and ML/AI integration. Expert Python team in Turkey.

E-commerce analytics platforms, fintech APIs, ML model services, CRM backends — 40+ projects delivered with Python.

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Our Python stack standards

Django 5 + Django REST Framework
FastAPI + Pydantic v2
PostgreSQL + Redis
Celery async task queue
Docker + CI/CD pipeline

100+

Projects shipped

10+

Years of experience

City coverage

4.9/5

Customer rating

01.

Why Python backend?

Python is the de facto standard for data-intensive applications, scientific computing, and machine learning integration. According to the Stack Overflow Developer Survey 2024, it's the third most popular language — and number one in fintech and AI.

Compared to Node.js, Python's strengths are native ML/AI libraries (NumPy, Pandas, scikit-learn, PyTorch), a mature ORM ecosystem, and depth in data-pipeline tooling. For API-heavy, thin backend projects Node.js is a valid choice; for analytics, ML integration, or heavy computation, Python is the natural pick.

02.

Enterprise backend with Django

Django's 'batteries included' philosophy ships a full ORM, auth, admin panel, cache layer, and migration system. The default choice for SaaS, ERP, and CRM projects with large data models.

On CreativeCode Django projects: custom user model, JWT + refresh token auth, role-based permissions, multi-tenant schema isolation, async ORM (Django 4.1+), Celery + Redis queue. Every project is delivered with 90%+ test coverage and OpenAPI 3 documentation.

03.

High-performance API with FastAPI

FastAPI handles 50,000+ requests per second with async Python. Automatic request/response validation via Pydantic v2, auto-generated OpenAPI schema, and interactive Swagger UI — zero extra config.

AI model serving, real-time scoring API, fintech payment endpoints, or high-traffic mobile backends — FastAPI is the standard pick. With horizontal scaling on Kubernetes, per-click cost in production stays on par with TypeScript alternatives.

04.

ML/AI model integration

A significant portion of Python projects now include an ML inference layer. OpenAI API, Hugging Face model, custom scikit-learn/XGBoost, or PyTorch model — connecting to a FastAPI endpoint is 1-3 days of work.

Batch prediction (Celery + Redis), real-time prediction (FastAPI async endpoint), vector search (pgvector, Pinecone) — we turn your data scientists' models into production APIs.

05.

Project process and delivery standards

Week one: technical requirements, existing system audit, data model design. Weeks 2-4: core domain logic, API layer, test coverage. Week 5+: staging environment, integration tests, monitoring setup.

Every delivery package includes: a locally runnable Docker Compose environment, .env.example documentation, Postman collection and OpenAPI spec, 90%+ unit test coverage, runbook.

Frequently asked questions

Should I choose Python or Node.js?

If the project is primarily data analysis, ML integration, or heavy computation — Python. For API-heavy, thin backend (mobile/web frontend service) projects, Node.js + TypeScript offers faster onboarding. We also work in hybrid architectures: Node.js main API + Python ML service.

Django or FastAPI?

Django: ORM-heavy, needs admin panel, complex auth/permission, large data model — enterprise/SaaS. FastAPI: high throughput, async, ML model serving, simple data model — AI backend and fintech API. Both can coexist in the same project.

How many weeks to ship a Python MVP?

6-10 weeks depending on data model and feature scope. FastAPI + PostgreSQL + basic auth skeleton: 1 week; core business logic: 3-4 weeks; test + staging + monitoring: 2-3 weeks.

How is security handled in Python projects?

SQL injection: parameterized queries (Django ORM / SQLAlchemy), never raw SQL strings. XSS: Pydantic validation, output encoding. Auth: JWT short-lived + refresh token rotation, bcrypt hash. Secrets: environment variables, never in code.

Can you take over an existing Python project?

Yes. We start with a code quality audit, test coverage measurement, and technical debt analysis. After the report we jointly define the takeover scope.

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